Pre-release

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GAFIME

PyPI version Python Versions License

GPU-Accelerated Feature Interaction Mining Engine.

GAFIME is a native feature interaction mining engine for tabular and structured machine-learning workflows. Python exposes a thin public API; Rust owns validation, planning, scheduling, CPU kernels, and report handles; CUDA, Metal, and ROCm/HIP own their backend-local C ABI payloads.

The engine is built for workloads where interaction candidates, decision-path regions, and temporal transforms become too expensive to search with ordinary Python loops or model-by-model trial code.

Install

Core package:

pip install gafime

Optional Python integrations:

pip install "gafime[sklearn]"
pip install "gafime[bench]"

Vendor GPU payloads are explicit in the v1 distribution design. Pip can select wheels by Python, ABI, OS, and CPU architecture, but not by local GPU vendor. CUDA and ROCm payloads therefore need explicit package selection once the split payload packages are published:

pip install "gafime[cuda]"
pip install "gafime[rocm]"

The extras install the separate PyPI payload projects gafime-cuda and gafime-rocm for the same GAFIME release.

Apple Silicon Metal follows the macOS arm64 wheel/platform path.

Detailed install and backend policy:

Basic Usage

from gafime import ComputeBudget, EngineConfig, GafimeEngine

config = EngineConfig(
    backend="auto",
    metric_names=("pearson", "r2"),
    budget=ComputeBudget(max_comb_size=2),
)

report = GafimeEngine(config).analyze(X, y, feature_names=feature_names)
print(report.backend)
print(report.interactions[:5])

Generate the reference notebook:

import gafime

gafime.generate_tutorial()

The generated notebook and the tracked practice notebook share the same v1 source. The repository also keeps a clearly labeled historical API notebook from earlier release work.

docs/notebooks/gafime_tutorial.ipynb (current v1 practice notebook)
docs/notebooks/gafime_full_api_reference_notebook.ipynb (historical reference)

Candidate Families

GAFIME supports:

continuous interaction candidates,
native decision-path candidates for threshold/region-like structure,
explicit time-series candidates: lag, delta, velocity, acceleration, rolling mean, rolling std, and rolling sum,
scikit-learn transformer integration through gafime.sklearn.GafimeSelector,
native Arrow ingest through gafime.dataload.

The v0.4 discrete candidate family has been deprecated and removed from the current engine path. Use decision-path candidates for tree-like threshold and region structure.

Decision-path bootstrap stability is supported, but permutation significance requires per-target path rediscovery and is not yet available. Set permutation_tests=0 when enabling decision-path generation; unsupported permutation requests fail closed rather than reporting invalid p-values.

Backend Policy

backend="auto" ranks the available native execution paths:

configured GPU payloads whose C ABI library loads and whose device_id reports valid device info,
the Rust CPU vector path ranked by detected ISA (AVX512 > AVX2 > SSE4.2/NEON),
the scalar Rust CPU path.

Explicit backend="cuda", backend="rocm", and backend="metal" never fall back to another backend. auto is the only mode that probes candidates and selects the best available execution path.

backend="gpu" is rejected because it is ambiguous across CUDA, ROCm, and Metal. Use auto, cuda, rocm, metal, or core.

Native Reports

Reports are structured Python objects. Read properties such as:

report.interactions
report.decision
report.backend
report.warnings

DiagnosticReport.to_dict() remains only as a deprecated export convenience. It should not be used as a runtime data-flow path.

Developer Docker Images

Docker files in this repository are development environments, not distribution images. Normal users should install GAFIME from PyPI wheels.

Available source-build containers:

docker compose run --build gafime-cuda-dev
docker compose run --build gafime-core-smoke

The CUDA development image includes the CUDA toolkit, compiler toolchain, Rust, CMake, GAFIME development/benchmark/scikit-learn dependencies, and the locally staged gafime-cuda payload by default. Extra workstation packages can be added with the EXTRA_PIP_PACKAGES Docker build argument. The Core smoke image is a smaller CPU-native source-build check.

Docker details:

CONTRIBUTING.md

Project References

docs/releases/v1.0.0b0.md
docs/releases/v1.0.0a0.md (previous alpha)
docs/capabilities.md
docs/eager-resident-compiled-execution.md
docs/backend-selection.md
docs/notebooks/gafime_tutorial.ipynb
USAGE.md
docs/notebooks/gafime_full_api_reference_notebook.ipynb (historical reference)
CONTRIBUTING.md

Maintainer release operations are documented separately in docs/releases/release-operations.md. That runbook does not authorize publication; release tags and uploads require an explicit maintainer decision from a fully validated commit.

Historical release records remain available under docs/releases/, including v0.4.7 and v0.5.0-legacy.

Contact

Maintainer: Hamza Usta

Email: hamzausta2222@gmail.com

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